Female Executives Leadership on Non-Efficiency Investment of Private Listed Companied in China
Bibliographic record
Abstract
The purpose of this paper is to study the relationship between female executives and non-efficient investment behavior in enterprises. The data of China's private listed companies from 2016 to 2017 were selected for empirical study, the variables of the model were defined and measured. The author designs the current research to be mixed methods research, qualitative and quantitative research approach. The results show that: (1) increase in the proportion of female CEOs relative to female executives can significantly inhibit non-efficient investment; (2) the level of education has a significant moderating effect on both female executives and the non-efficient investment of female CEOs and companies; (3) Social capital has a moderating effect on the non-efficient investment of female executives and enterprises, but has no significant effect on the gender of CEOs and non-efficient investment of enterprises. The conclusion of this study can better break the bottleneck of female workplace, develop the leadership of female executives in a targeted way, and improve the management level and performance of enterprises through the relationship between female executives and non-efficient investment behaviors of enterprises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".